A Light-weight Transformer-based Self-supervised Matching Network for Heterogeneous Images

TL;DR

This paper introduces LTFormer, a lightweight transformer-based self-supervised network for matching heterogeneous remote sensing images, effectively handling radiometric differences without large annotated datasets.

Contribution

It presents a novel self-supervised matching network with a lightweight transformer and a new triplet loss, improving robustness and performance in heterogeneous image matching.

Findings

Outperforms traditional hand-crafted descriptors

Competitive with state-of-the-art supervised methods

Effective with limited annotated data

Abstract

Matching visible and near-infrared (NIR) images remains a significant challenge in remote sensing image fusion. The nonlinear radiometric differences between heterogeneous remote sensing images make the image matching task even more difficult. Deep learning has gained substantial attention in computer vision tasks in recent years. However, many methods rely on supervised learning and necessitate large amounts of annotated data. Nevertheless, annotated data is frequently limited in the field of remote sensing image matching. To address this challenge, this paper proposes a novel keypoint descriptor approach that obtains robust feature descriptors via a self-supervised matching network. A light-weight transformer network, termed as LTFormer, is designed to generate deep-level feature descriptors. Furthermore, we implement an innovative triplet loss function, LT Loss, to enhance the matching performance further. Our approach outperforms conventional hand-crafted local feature descriptors and proves equally competitive compared to state-of-the-art deep learning-based methods, even amidst the shortage of annotated data.